Rheologica Acta最新文献

Squeeze flow has been proven as an interesting technique for the rheological evaluation of many classes of materials, being relatable to common compressive phenomena from various processing and application procedures. Despite the simplicity of the experimental setup needed to run it, the results from the test are rather complex, involving multiple variables and factors that are not fully clarified by the bulk stress response. One additional piece of information that can be valuable is the pressure distribution over the sample area, since it is related to key aspects of the flow. The addition of a pressure mapping system to the traditional setup of the test has been recently proposed as a way to enrich the information obtained, in a method deemed pressure mapped squeeze flow (PMSF). This paper presents the evolution and state of the art of this technique, and analyzes a plastic clay with two different water contents in three displacement rates to demonstrate the potential and possibilities that PMSF offers. The experimental setup is presented in detail, along with the calibration procedure and data treatment suggested, as well as multiple types of analyses including bulk stress curves, raw pressure distribution plots, measured contact area, evolution of the mean profile, comparison to theoretical models supported by error analysis, and investigation of variation over the area. With the procedure established and presented in this work, it should be possible to apply PMSF as a valuable technique throughout the materials science and engineering community.

Recent experimental observation in fast extensional flow of polymer melts and solutions displayed as presence and absence of viscosity thinning, respectively, has necessitated and also initiated nonlinear modification of the Rouse model, the fundamental molecular model for unentangled polymeric liquids. On that account, concept of reduction of bead friction is introduced in the form of variable friction coefficient (zeta (t)) sometimes with corresponding variation of the Brownian force. This work presents mathematical constraint based on reasonable assumptions for volume conservation of deforming chains and accordingly formulates the rheological constitutive equation. The equation of constraint for volume conservation possibly relieves in part the complication introduced by the friction reduction and intrinsic flow-induced anisotropy in nonlinear modification of the Rouse model. The suggested constitutive equation expresses description in simple rheometric flows quite similar to that of the previous model with B-variation given by Sato et al. (2021) when both are formulated with effects of finite extensibility (FENE) and friction reduction. In addition, the molecular dynamics simulation demonstrates possible validity of the current hypothesis, the constraint of chain volume conservation.

Graphical abstract

Numerical simulations are used to study the dynamics of a developing suspension Poiseuille flow with monodispersed and bidispersed neutrally buoyant particles in a planar channel, and machine learning is applied to learn the evolving stresses of the developing suspension. The particle stresses and pressure develop on a slower time scale than the volume fraction, indicating that once the particles reach a steady volume fraction profile, they rearrange to minimize the contact pressure on each particle. We consider the timescale for stress development and how the stress development connects to particle migration. For developing monodisperse suspensions, we present a new physics-informed Galerkin neural network that allows for learning the particle stresses when direct measurements are not possible. We show that when a training set of stress measurements is available, the MOR-physics operator learning method can also capture the particle stresses accurately.

This study presents the development of an artificial neural network (ANN) model to predict the viscosity of ethylene–glycol based nanofluids with different types of nanoparticles using four input parameters: nanoparticle type, size, concentration, and temperature of measurement. The model was trained and validated using 470 experimental measurements. The ANN model demonstrated high accuracy in predicting the viscosity of nanofluids. The obtained statistical error metrics between the measured and predicted values of viscosity were found to be very low. MAPE values were equal to 1.19% and 2.33% for training and testing respectively. The developed model can help researchers to better understand EG-based nanofluids viscosity behavior, and this could be considered as a good step forward to help researchers design new nanofluids with enhanced properties. To make the model more accessible for engineers and researchers, a user-friendly web application was developed using Angular and Django, allowing users to input parameters and obtain viscosity predictions without dealing with complex code. The web application offers multiple output options, including figures, tables, and Excel files. This multidisciplinary research study combines web technology, data science, and fluid mechanics to provide a valuable tool to predict nanofluids’ viscosity for different input parameters.

Graphical abstract

A multi-step, iterative technique for the local non-parametric identification of the standard linear solid (SLS) material model employing fractional order time differential operators is presented. Test input data consists of a set of identified material complex modulus values estimated at different frequency values, obtained from input–output experimental measurements made on a material specimen by means of forced harmonic excitation and from experimental measurements made on the same specimen in quasi-static relaxation conditions. The proposed technique is mainly based on an algebraic procedure leading to the solution of an overdetermined system of linear equations, in order to get the optimal value of the model unknown parameters. The procedure is non-parametric, since the SLS model order is initially unknown. The optimal model size can be found by evaluating the stability properties of the solution associated to any model size and by automatically discarding computational, non-physical contributions. The identification procedure is first validated by means of numerically simulated test data from within known model examples, and then it is applied to some experimentally obtained test data associated to different materials.

The electroosmotic flow of a viscoelastic fluid in a capillary system was investigated analytically. The rheology of the fluid was characterized by a novel generalized exponential model equation. The charge density obeys the Boltzmann distribution, which governs the electrical double-layer field and body force generated by the applied electrical field. Mathematically, this scenario can be modeled by the Poisson-Boltzmann partial differential equation, by assuming that the zeta potential is small, i.e., less than 25 mV (Debye-Hückel approximation). Considering a pulsating electric field, the shear viscosity and the alteration in the volumetric flow were presented as a function of the material parameters through the characteristic dimensionless numbers by using an exponential-type generalized rheological model. Thixotropy, shear thinning, yield stress mechanisms, and weight concentration were analyzed through numerical results. Finally, the flow properties and rheology were predicted using experimental data reported elsewhere for worm-like micellar solution of cetyl trimethyl ammonium tosilate (CTAT). The rheological equation of state proposed in this study describes the alterations in the structure resulting from applied forces (tangential and normal). These forces induced a structural evolution (kinetic model) due to the relaxation processes caused by shear strain. It is important to mention that in electroosmotic flows, complex behavior such as (i) thixotropy, (ii) rheopexy, and (iii) shear banding flow is scarcely explained in terms of the change in the structure of the fluid under flow.

Graphical Abstract

Developments in the last century, and especially in the last 50 years, have advanced understanding of suspension rheology greatly. Here, a limited review of suspension work over this period is presented, emphasizing advances over the last three decades in understanding of the particle pressure and strong shear thickening, which were motivated by crucial experimental observations, computational advances, and a critical review, all from the 1980s. This review serves as a preview to some outstanding challenges in suspension mechanics. This article considers primarily dispersions of spherical particles, which serve not only as a model material for understanding the rheology of more complex fluids of practical relevance, but also as a basic system for the study of nonequilibrium statistical physics.